Gutell 122.chapter comparative analy_russell_2013

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Gutell R.R. (2013). …

Gutell R.R. (2013).
Comparative Analysis of the Higher-Order Structure of RNA.
in: Biophysics of RNA Folding. Volume editor: Rick Russell. Series title: Biophysics for the Life Sciences. Series editors: Norma Allewell, Ivan Rayment, Bertrand Garcia-Moreno, Jonathan Dinman, and Michael McCarthy. pp. 11-22. Publisher: Springer, New York, NY.

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  • 1. 11R. Russell (ed.), Biophysics of RNA Folding, Biophysics for the Life Sciences 3,DOI 10.1007/978-1-4614-4954-6_2, © Springer Science+Business Media New York 2013Abstract“If you want to understand function, study structure”The functions of many RNAmolecules are directly associated with their higher-orderstructure, and given the vast abundance of their functions in a cell, the determinationof their structures should contribute significantly to our understanding of the cell.A variety of methods are used to determine their higher-order structure. A multitudeof experimental methods are discussed elsewhere in this book. Computationalmethods are also used. The first type, considered one of the grand challenges in biol-ogy, utilizes different fundamental principles of RNA structure to predict their sec-ondary and three-dimensional structure. While the accuracies of these methods havebeen improving, generally speaking, higher-quality structure information is obtainedwith experimental methods. In contrast to the computational methods that utilizefirst principles to predict the RNA’s higher-order structure, comparative methodsare utilized to infer structure, function, and evolution from the patterns of sequenceand structure conservation and variation. The primary objective of this chapter is tobriefly review the use of comparative analysis to deduce information about RNAstructure, using an evolutionary framework.Keywords RNA structure • Computational comparative analysis • RNA structuremotifs • Covariation analysisR.R. Gutell(*)Integrative Biology, Institute for Cellular and Molecular Biology, and the Centerfor Computational Biology and Bioinformatics, University of Texas at Austin,PAT 141, 2401 Speedway, Austin, TX 78712, USAe-mail: robin.gutell@mail.utexas.eduChapter 2Comparative Analysis of the Higher-OrderStructure of RNARobin R. Gutell
  • 2. 12 R.R. Gutell2.1 Fundamental Changes in Our Understanding of RNAStructure and FunctionThe central dogma, enunciated by Crick in 1958 and the keystone of molecular biologyever since, is likely to prove a considerable oversimplification (Anonymous 1970).Since the central dogma in molecular biology was established (Crick 1958), theprimary role for RNA has been associated with protein synthesis. Molecular biolo-gists, at the onset of the discovery of replication, transcription, and translationfocused primarily on proteins and DNA, since it was already known that proteinscan form three-dimensional structures that catalyze reactions, and DNA was knownto contain the instructions to make the proteins. Within this process, the primaryrole for RNA was thought to be the messenger RNA, which carries the informationfrom the DNA to the ribosome to code for the proteins. In addition, it was knownthat transfer RNAs assign amino acids to their proper codon assignment, and ribo-somal RNA is part of the ribosome. Still, these RNAs were initially perceived assimply coding and structural, not dynamically involved in catalytic functions, andwhile this central dogma is still correct, it primarily reveals only the protein’s andDNA’s role in the metabolism and regulation of the cell. The significance of RNAstructure and function in the cell had been minimal with the central dogma as articu-lated in 1958 (Crick 1958).Dennis Overbye stated in the New York Times (July 27, 2011, (http://www.nytimes.com/2011/07/28/science/28life.html?_r=4&ref=science) “… RNA, orribonucleic acid, … plays Robin to DNA’s Batman in Life As We Do Know It,assembling proteins in accordance with the blueprint encoded in DNA.” Whileour understanding of RNA’s structure and function did not change for the first 20or so years after the central dogma was proposed, it was postulated, based ontheoretical considerations, that RNA came before DNA and proteins (Woese1967; Crick 1968; Orgel 1968). RNA has characteristics of DNA and protein.RNA, like DNA, has similar rules for base pairing - adenine pairs with uracil(thymine) and guanine pairs with cytosine. ‘Canonical’ base pairs that are con-secutive and antiparallel on an RNA sequence form standard helices, and likeproteins, RNA forms three-dimensional structures, which for RNA are composedof helices, hairpin, internal, and multistem loops, and other structural motifs(Moore 1999).Experimental evidence, beginning in the 1970s, started to suggest that rRNAwasdirectly involved in protein synthesis (Noller and Chaires 1972). During the early1980s a series of studies revealed that the group I intron and RNase P were directlyinvolved in the chemical catalysis of RNA (Kruger et al. 1982; Guerrier-Takadaet al. 1983). Subsequently, other RNAs were identified and characterized that cata-lyze chemical reactions, including riboswitches (Haller et al. 2011; Breaker 2012),while it was determined that RNA has the capacity of catalyzing many differenttypes of chemical reactions (Hiller and Strobel 2011), including the primary stepsin decoding and peptidyl transferase during protein synthesis (Moore and Steitz
  • 3. 132 Comparative Analysis of RNA2011) (Ogle et al. 2001; Noller 2006). Beyond the many functions of RNA’sthree-dimensional structures, small and large RNAs are being implicated in theregulation of nearly all of the cell’s functions. Accordingly different RNAs are associ-ated with many diseases and other anomalies in the cell.2.2 Comparative Analysis: An IntroductionIn the 1830s Darwin used comparative analysis to identify patterns in the anatomicalfeatures of some animals and in the process determined fundamental principlesabout the evolution of biological species (Darwin 1859). More recently, compara-tive analysis has been used to study macromolecular structure. Once the first fewtransfer RNA sequences were determined in the early 1960s, it was appreciated thatthe three-dimensional structures of tRNA would be very similar although theirnucleic acid sequences could share little identity with one another. The cloverleafsecondary structure of tRNA, with approximately 76 nucleotides, was determinedto be common to all of the known tRNA sequences (Holley et al. 1965; Madisonet al. 1966; RajBhandary et al. 1966). Subsequent analysis revealed that the prob-ability that 14 tRNA sequences could all form the same cloverleaf secondary struc-ture by coincidence is 1 in 1020(Levitt 1969). This latter analysis also revealed afew tertiary-structure interactions. This approach to the determination of RNA’ssecondary structure was substantiated when the proposed tRNA secondary struc-ture and a few of the tertiary-structure base pairs were confirmed with crystallogra-phy (Kim et al. 1974; Robertus et al. 1974). This success with tRNA was thefoundation for comparative methods to be utilized for the identification of higher-order structures that are conserved in different RNAfamilies. In 1975, the secondarystructure for 5S ribosomal RNA, a molecule approximately 120 nucleotides long,was initially proposed with comparative methods (Fox and Woese 1975).Subsequently, once a few 16S and 23S rRNA sequences were determined in the late1970s and early 1980s, the minimal secondary-structure models were determinedfor these RNAs that are approximately 1,540 and 2,900 nucleotides long in bacteria(Woese et al. 1980; Branlant et al. 1981; Glotz et al. 1981; Noller et al. 1981;Stiegler et al. 1981; Zwieb et al. 1981). During the 1980s, other RNA moleculeswere studied with comparative methods, including group I (Cech 1988; Michel andWesthof 1990) and II (Michel et al. 1989) introns, ribonuclease (RNase) P RNA(James et al. 1988), U-RNAs (U1, U2, U4, U5, and U6) (Guthrie and Patterson1988), 7S SRP RNA (Zwieb 1989), and telomerase RNA (Romero and Blackburn1991). More recently, the secondary structures for many other RNA types havebeen elucidated with comparative analysis (Gardner et al. 2009) due to our currentappreciation that RNA is directly involved in many, if not all, of the regulations inthe cell, and the advent of ultrarapid nucleic acid sequencing that is providing uswith the genetic blueprints for a very large number of organisms that span acrossthe entire tree of life.
  • 4. 14 R.R. Gutell2.3 Covariation Analysis: Identification of Canonicaland Noncanonical Base PairsThe primary method for the identification of a common structure is based on a verysimple principle. While the primary structure (or sequence) of RNAs within thesame family can have significant variation with one another, base pairing, the domi-nant element in RNA structure, can be conserved in the secondary and three-dimen-sional structure of RNAs.As a consequence, a very large number of RNAsequencescan be mapped to the same secondary and three-dimensional structure. In practice,the most common means to determine this common structure is from the analysis ofthe patterns of variation in an alignment of the sequences. Initially, when the num-ber of sequences in an alignment was small, sub-sequences that had the potential toform G:C, A:U, and G:U base pairs within a helix were identified. Those potentialhelices with at least two exchange (or covariation) of one canonical base pair withanother were considered a possible helix (Noller et al. 1981). As the number ofsequences in an alignment increased, covariation algorithms were developed toidentify those positions with similar patterns of variation (Olsen 1983; Gutell et al.1985, 1992; Gautheret et al. 1995). These latter methods did not specifically searchfor G:C, A:U, and G:U base pairs that occur within a potential canonical helix.The most recent comparative structure model for Escherichia coli 16S rRNA(Fig. 2.1) is the culmination of approximately 30 years of comparative analysis. Thecoloring of the base pair symbols reveals our confidence for every proposed basepair shortly prior to the determination of the high-resolution crystal structures (seebelow). Red indicates the base pairs with the most significant covariation (strongestconfidence), followed by green and black. Black indicates a minimal amount ofcovariation and/or variation at one of the paired positions but no correspondingvariation at the other paired position, and gray and blue indicate nucleotide conser-vation greater than 98% for G:C, A:U, and G:U base pairs within a canonical orcompound helix that has strong support of other base pairs. The same coloring foreach of the proposed base pairs is associated with the base-pair-frequency tables atthe Gutell lab’s Comparative RNA Web (CRW) Site [http://www.rna.ccbb.utexas.edu/SAE/2A/nt_Frequency/BP/16S_Model]. Note that the vast majority of the pro-posed base pairs in 16S rRNA have a red base-pair symbol. The small number ofblack, gray, and blue usually occurs at the ends of the helices.The results from these covariation methods were very profound. While themajority of the sets of positions with similar patterns of variation in the rRNAscontained G:C, A:U, and G:U base pairs that occur within a helix, a small numberof covariations contained base pairs that were irregular (Gutell 1993; Gutell et al.1994). Thus, covariation analysis, a specific type of comparative analysis, hasindependently identified two of the most fundamental principles of nucleic acidstructure: (1) base pairings that are composed of G:C, A:U, and G:U, and (2) thesebase pairs are arranged adjacent and antiparallel with one another to form a helix.Given this recapitulation of these two canonical structural elements in RNA, weare compelled to accept, or at least seriously consider, the noncanonical structural
  • 5. 152 Comparative Analysis of RNAelements identified from the covariation analysis of the rRNAs. These include thefollowing:• Non-Canonical base pairs: Several types of noncanonical base-pair exchangeshave been identified. The most common exchanges are A:A <> G:G (i.e.,exchanges between A:A and G:G pairs), G:U <> A:C, C:C <> U:U, A:G <>G:A, and G:U <> A:C. These noncanonical base pairs usually occur at the end ofa regular canonical helix or as a lone pair not flanked by other base pairs.Symbols Used In This Diagram:G − C - Canonical base pair (A-U, G-C)G · U - G-U base pairG ∞ A - G-A base pairU • U - Non-canonical base pairEvery 10th nucleotide is marked with a tick mark,and every 50th nucleotide is numbered.Tertiary interactions with strong comparative dataare connected by solid lines.Fig. 2.1 Comparative Escherichia coli 16S ribosomal RNA secondary-structure model (Cannone,Subramanian et al. 2002)[http://www.rna.ccbb.utexas.edu/]
  • 6. 16 R.R. Gutell• Lone Pairs: A Lone pair (individual or isolated base pairs) is not flanked bya base pair on its 5¢ or 3¢ end. Lone pairs are not stable enough to occur withoutadditional interactions, such as base stacking or being flanked by nucleotidesinvolved in a tertiary interaction. They occur in several structural environments,including internal loops, multistem loops, and between two hairpin loops (oneform of a pseudoknot).• Lone-pair tri-loops: A special class of lone pairs contains a single base paircapped by a hairpin loop with only three nucleotides. Several of these that wereidentified with covariation analysis occur in the rRNAs (Gutell 1996). All ofthese are immediately 3¢ to a secondary-structure helix, suggesting that theyform a coaxial stack with the 5¢ helix. An analysis of the high-resolution crystalstructure of the rRNAs revealed that this motif occurs frequently in the rRNAs,and all of them are 3¢ to an existing helix and all of them are coaxially stackedonto this helix (Lee et al. 2003).• Pseudoknots: Pseudoknots are defined as at least one base pair that crosses asecondary-structure helix. These usually vary from one to three base pairs inlength in the rRNAs, and are usually always immediately adjacent to a secondary-structure helix, suggesting that they can form a coaxial stack with these adjacenthelices. Nearly 20 pseudoknots were identified with covariation analysis in therRNAs (Gutell et al. 1986; Gutell and Woese 1990; Alkemar and Nygard 2003).• Parallel arrangement of base pairs: While nearly all of the adjacent base pairsare arranged antiparallel with one another, a few of the base pairs identified withcovariation analysis are parallel. The most prominent example occurs in domainV of 23S rRNA. Here positions 2112:2169, 2113:2170, and 2117:2172 formbase pairs. While 2112:2169 contains A:G and G:A base pair types, the lattertwo base pairs exchange primarily between C:G <> U:A and G:C <> A:U,respectively (Gutell 1993).• Base triples: Covariation analysis has identified several base pairs that covarywith a third “unpaired” nucleotide. The best candidates include the following: (1)in the 16S rRNA - position 121 with either the 124:237 or 125:236 base pair, 863with the 570:866 base pair, position 595 with the 596:644 base pair, and (2) in the23S rRNA: position 2011 with the 2144:2147 base pair and between positions1072 and the 1092:1099 base pair (Gautheret et al. 1995; Conn et al. 1998).• Non-base-pair constraints: While all of the previous constraints (or dependenciesbetween the evolution of different positions) are associated with a base pair,covariation analysis has also identified weaker albeit significant covariationsbetween positions that are not base paired. One of the first examples, initiallypublished in 1992 and elaborated on thereafter (Gutell et al. 1992; Gutell 1993;Gautheret et al. 1995), revealed that eight of the nucleotides in the D helix and thevariable loop co-evolve in the type-1 tRNAs. Our rationale for this set of eightco-evolving nucleotides is associated with the structural and evolutionarydynamics of several base triples with several consecutive base pairs. Thisstructural constraint restricts the types of changes that can occur at other posi-tions that are in close proximity in three dimensions. Other examples of non-base-pair constraints have been identified in the group I intron and the rRNAs(Shang et al. 2012).
  • 7. 172 Comparative Analysis of RNA2.4 Accuracy of the Covariation-Based Higher-OrderStructureAsnotedearlier,thecomparativesecondary-structuremodelfortRNAwassubstantiatedwith its high-resolution crystal structure (Kim 1976; Rich and RajBhandary 1976).While all of the secondary-structure base pairs and a few of the tertiary interactionspredicted with comparative analysis were in the crystal structure, several tertiary-structure interactions in the crystal structure were not identified with comparativeanalysis. The comparative structure models for 5S, 16S, and 23S ribosomal RNA,the culmination of more than 30 years of an initial predicted structure followed byrefinements as the number of sequences and the diversity of the sequences increased,and multiple improvements in the covariation algorithms (Cannone et al. 2002)were compared with the high-resolution crystal structures that were determined in2000 (Ban et al. 2000; Wimberly et al. 2000). Of the 476 base pairs in the predicted16S rRNAsecondary-structure model, including a small number of tertiary-structureinteractions (e.g., noncanonical base pairs, base triples, base pairs not in canonicalhelices), 461 (or 97%) were in the 30S ribosomal crystal structure. Of the 797 basepairs in the 23S rRNA secondary-structure model (including the small number oftertiary-structure interactions), 779 (or 98%) were in the 50S ribosomal crystalstructure. Nearly all of the base pairs that were predicted with comparative methods,but not in the high-resolution crystal structure, were base pairs with minimal or nocovariation, and, accordingly, those base pairs with sufficiently large amounts ofcovariation were present in the crystal structure (Gutell et al. 2002). Our analysis ofthe crystal structures of the ribosome also revealed 56 and 425 base-base interactionsin the 16S and 23S rRNA, respectively, that were not predicted with comparativeanalysis. An analysis did not reveal any significant covariation in nearly all of thesebase pairs first identified in the crystal structures (Shang et al. 2012).2.5 Structural MotifsComparative analysis reveals more than just the base pairs that have covariation attwo paired positions. Comparative analysis has been used to identify structuralmotifs that are the basic building blocks of RNA structure. Earlier it was noted thatcovariation analysis has independently determined two of the most fundamentalprinciples of RNA structure: (1) the base pair and the most frequent pairing types,G:C, A:U, and G:U, and (2) the arrangement of consecutive and antiparallel basepairsintoahelix.Covariationanalysisalsorevealedseveralothertypesofnoncanonicalbase-pair types and noncanonical arrangements of base pairs in context with otherstructural elements. All of these “non-canonical” base pairs were present in the high-resolution crystal structure. We now question if comparative analysis can be utilizedto identify structural elements that are present in similar structural environments
  • 8. 18 R.R. Gutellthat do not have a covariation signal. Below are a few of the many structural motifsthat have been identified.Unpaired Adenosines: An analysis in 1985 revealed that approximately 66% of theadenosines in the Escherichia coli 16S rRNA comparative secondary-structuremodel were unpaired, while only approximately 30% of the G’s, C’s, and U’s wereunpaired (Gutell et al. 1985). A more comprehensive analysis in 2000 revealed thatthese biases in the distribution of the four nucleotides in a large sampling of bacte-rial 16S and 23S rRNAs were approximately the same as for E. coli 16S rRNA. Thisstudy revealed many other biases in the distribution of nucleotides in the paired(helices) and unpaired (loops) regions. A few biases worthy of mention are as fol-lows: (1) more than 50% of the 3¢ ends of a loop contain an A that is conserved atthat location in more than 95% of the sequences, (2) G and A are the two most fre-quent nucleotides at the 5¢ ends of a loop, and (3) the most frequent consecutivenucleotides are GG, GA, AG, and AA, with ~70% of the GG occurring within ahelix, ~70% of the AA occurring in loops. These and other observations from thisanalysis are consistent with the distribution of nucleotides in GNRA tetraloops (seebelow), adenosine platforms (Cate et al. 1996), E and E-like loops, and AA and AGjuxtapositions flanking the ends of a helix (see below) (Gutell et al. 2000).Tandem GA & AA.AG@helix.ends: Studies have revealed that tandem G:A juxtapo-sitions occur frequently within helices. The most frequent orientation of the tandemG:A juxtapositions is 5¢ N:N¢ G:AA:G M:M¢ 3¢, where N and N¢, and M and M¢ canbe any set of canonical base pairs flanking the tandem GAs. The G is 3¢ to a nucle-otide that is base paired within a helix and the A is 5¢ to a nucleotide that is basepaired within a helix. The G is frequently exchanged with an A. The G:A and A:Ajuxtapositions usually form the sheared conformation when this tandem is within ahelix. The G is rarely 5¢ to a nucleotide that is base paired within a helix (SantaLuciaet al. 1990) (Gautheret et al. 1994). It was also observed that in the ribosomal RNAs,helices are frequently flanked by a G:A juxtaposition on the loop side of a canonicalhelix (Traub and Sussman 1982) (Elgavish et al. 2001). With a significantly largerdataset of comparative rRNA secondary structures, it was observed that the Gs inthe G:A juxtaposition are replaced with an A. The G in the G:A juxtaposition isnearly always at the 3¢ end of the helix. The majority of these A:A and A:G at theends of helices form a base pair (sheared conformation) in the 16S and 23S rRNAcrystal structures. The AA & AG at helix end motif occurs within several largerstructural motifs—GNRA tetraloops (see below), E and E-like loops, tandem G:Abase pairs, U-turns (see below), and adenosine platforms (Cate et al. 1996).Tetraloops: It was observed that the sequence for the hairpin loop with four nucle-otides is frequently GNRA in the group I introns (Michel and Westhof 1990). Themajority of the hairpin loops in the rRNAs have four nucleotides, and the majorityof these have the GNRA tetraloop sequence (where N is any nucleotide and R is apurine, either A or G) (Woese et al. 1990). While this GNRA tetraloop is the mostfrequent hairpin loop with four nucleotides, tetraloops with the sequences UUCGand CUUG also occur frequently in the rRNAs (Woese et al. 1990). Nearly all of the
  • 9. 192 Comparative Analysis of RNAGNRA tetraloops in the ribosomal RNAs are involved in a tertiary interaction.Comparative analysis reveals the conservation and variation at each of the tetraloopsfor any portion of the phylogenetic tree. While some tetraloops are invariant, othersexchange primarily between the different sequences within the GNRA family, andothers exchange between the GNRA, UUCG, CUUG, and possibly other sequences.Notable is the tetraloop at position 83–86 in 16S rRNA. The primary sequencesobserved are GCAA, UUCG, and CUUG. The rate of exchanges between thesesequences is high, as gauged by mapping these tetraloop sequences onto the phylo-genetic tree. All three sequences are present in all of the major phylogenetic groupsin the bacteria [see Table 1 in (Woese et al. 1990)]. The different rates of evolutionof the tetraloops and the different compositions present at each tetraloop locationsuggest that tetraloops have different functions. While the primary tetraloopsequences are known to be more stable than other hairpin loops, UUCG tetraloopsare known to be particularly stable (Tuerk et al. 1988) and are likely to be nucleatingthe formation of a helix during RNA folding. In contrast, nearly all of the GNRAtetraloops in the rRNA crystal structures form tertiary-structure interactions.2.6 Future ProspectsThese examples of structural motifs in RNA are only a partial list. They reveal someof the utilities and latitude that comparative analysis offers. At this stage we addressa variety of questions to assess the full potential of comparative analysis.The operational premise for comparative analysis is based on one of the majordiscoveries in molecular biology since the elucidation of the double helix. Thethree-dimensional structure of proteins and nucleic acids can remain relatively con-stant during significant evolutionary changes in the macromolecule’s primary struc-ture. While this premise is widely used in the study of many macromolecules andtheir functions in the cell, its full extent has not been fully explored.What is the relationship between RNA’s sequence variation and the variation in itssecondary structure and its three-dimensional structure?What is the maximum amountof variation that is possible between two sequences in the same RNA family?While the positions that form secondary-structure base pairs generally covarywith one another, the majority of the positions that form tertiary-structure base pairsdo not have a simple covariation. The pattern of covariation in secondary-structurebase pairs is simple. Do the tertiary-structure base pairs have patterns of variationthat can be deciphered and utilized to predict these base pairs with comparativesequence and structure information?Can comparative methods be used to identify more structural motifs that do nothave any obvious positional covariation? Ultimately we wonder how much of thehigher-order structure for an RNA can be inferred with comparative methods?Acknowledgements The author appreciates the funding from the National Institutes of Health(GM067317) and the Welch Foundation (F-1427) to support the research in the Gutell lab.
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  • 12. Biophysics for the Life SciencesSeries editorNorma AllewellFor further volumes:http://www.springer.com/series/10230
  • 13. Rick RussellEditorBiophysics of RNA Folding
  • 14. ISBN 978-1-4614-4953-9 ISBN 978-1-4614-4954-6 (eBook)DOI 10.1007/978-1-4614-4954-6Springer New York Heidelberg Dordrecht LondonLibrary of Congress Control Number: 2012952011© Springer Science+Business Media New York 2013This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformation storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed. Exempted from this legal reservation are briefexcerpts in connection with reviews or scholarly analysis or material supplied specifically for thepurpose of being entered and executed on a computer system, for exclusive use by the purchaser ofthe work. Duplication of this publication or parts thereof is permitted only under the provisions of theCopyright Law of the Publisher’s location, in its current version, and permission for use must alwaysbe obtained from Springer. Permissions for use may be obtained through RightsLink at the CopyrightClearance Center. Violations are liable to prosecution under the respective Copyright Law.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exemptfrom the relevant protective laws and regulations and therefore free for general use.While the advice and information in this book are believed to be true and accurate at the date ofpublication, neither the authors nor the editors nor the publisher can accept any legal responsibilityfor any errors or omissions that may be made. The publisher makes no warranty, express or implied,with respect to the material contained herein.Printed on acid-free paperSpringer is part of Springer Science+Business Media (www.springer.com)EditorRick RussellDepartment of Chemistry and BiochemistryUniversity of TexasAustin, TX, USA
  • 15. vContents1 Introduction and Overview.................................................................. 1Rick Russell2 Comparative Analysis of the Higher-OrderStructure of RNA .................................................................................. 11Robin R. Gutell3 Graph Applications to RNA Structure and Function........................ 23Namhee Kim, Katherine Niccole Fuhr, and Tamar Schlick4 Prediction and Coarse-Grained Modelingof RNA Structures................................................................................. 53Zhen Xia and Pengyu Ren5 Studying RNA Folding Using Site-DirectedSpin Labeling......................................................................................... 69Xiaojun Zhang and Peter Z. Qin6 The RNA Recognition Motif and Messenger RNA ............................ 89Kathleen B. Hall7 Memory Effects in RNA Folding DynamicsRevealed by Single-Molecule Fluorescence ........................................ 117Rui Zhao and David Rueda8 An Integrated Picture of HDV Ribozyme Catalysis .......................... 135Barbara L. Golden, Sharon Hammes-Schiffer,Paul R. Carey, and Philip C. Bevilacqua9 Combining Biochemical and Structural Informationto Model RNA-Protein Complex Assembly ........................................ 169Maithili Saoji, Chun Geng, and Paul J. Paukstelis
  • 16. vi Contents10 Following RNA Folding From Local andGlobal Perspectives............................................................................... 187Michael Brenowitz and Lois Pollack11 The Roles of Chaperones in RNA Folding .......................................... 205Pilar Tijerina and Rick RussellIndex............................................................................................................... 231